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Introduction

This document describes the technical methodologies and implementation of the climate and energy indicators underpinning the C3S Energy operational service. The tasks undertaken were geared to the provision of data and software to ECMWF that is deemed to be compliant with the protocol adopted by the CDS. Regular contact with the ECMWF technical teams was maintained to assist in the installation of all software components and data on the CDS infrastructure.

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2 https://preview.entsoe.eu/data/power-stats/
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https://preview.entsoe.eu/data/power-stats/hourly_load/
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https://transparency.entsoe.eu

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Bias adjusted datasets, daily for precipitation and 3-hourly for the other variables, were saved to NetCDF files, quality checked and standardized to be compliant with the DMP. The CMOR standardization was used for consistency with ESGF standards. Each file has a size of ~ 50-60 Gb. They are made available and uploaded to ESGF (https://esgf-node.ipsl.upmc.fr/search/cordex-ipsl/).

NUTS computation

Datasets of daily values at the country (NUTS0) and regional (NUTS2) level are made available for climate indicators following the methodology described in Section 1.1.1. The wind speed at 10 and 100 metre heights is also aggregated over maritime regions, defined at two different levels, denoted MAR0 and MAR1, in analogy with NUTS0 and NUTS1 (clusters of regions) levels.

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9 ICES : International Council for the Exploration of the Sea
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https://www.ices.dk/marine-data/maps/Pages/ICES-statistical-rectangles.aspx

Unfortunately, the ICES statistical rectangles don't cover the entire Mediterranean Basin. To cope with this limited domain, the GFCM statistical rectangles could be used instead for the Mediterranean region. These rectangles that can be downloaded from http://www.fao.org/fileadmin/user_upload/faoweb/GFCM/Maps/GFCM_Statistical_grid.zip are represented in the right map of Figure 61. It can be observed that these rectangles are small than that of the ICES dataset. In addition, a region of the north Atlantic is not covered by the high resolution rectangles. An additional dataset or a manual extension of the available data is therefore needed here.

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Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.

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This document has been produced in the context of the Copernicus Climate Change Service (C3S).

The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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